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  1. Models for Prediction, Explanation and Control: Recursive Bayesian Networks.Lorenzo Casini, Phyllis McKay Illari, Federica Russo & Jon Williamson - 2011 - Theoria : An International Journal for Theory, History and Fundations of Science 26 (1):5-33.
    The Recursive Bayesian Net formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how (...)
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  • A Multi-Scale View of the Emergent Complexity of Life: A Free-Energy Proposal.Casper Hesp, Maxwell Ramstead, Axel Constant, Paul Badcock, Michael David Kirchhoff & Karl Friston - forthcoming - In Michael Price & John Campbell (eds.), Evolution, Development, and Complexity: Multiscale Models in Complex Adaptive Systems.
    We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of (...)
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  • Identifying Intervention Variables.Michael Baumgartner & Isabelle Drouet - 2013 - European Journal for Philosophy of Science 3 (2):183-205.
    The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention (...)
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  • An Informational Theory of Counterfactuals.Danilo Fraga Dantas - 2018 - Acta Analytica 33 (4):525-538.
    Backtracking counterfactuals are problem cases for the standard, similarity based, theories of counterfactuals e.g., Lewis. These theories usually need to employ extra-assumptions to deal with those cases. Hiddleston, 632–657, 2005) proposes a causal theory of counterfactuals that, supposedly, deals well with backtracking. The main advantage of the causal theory is that it provides a unified account for backtracking and non-backtracking counterfactuals. In this paper, I present a backtracking counterfactual that is a problem case for Hiddleston’s account. Then I propose an (...)
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  • Confirmation and the Generalized Nagel–Schaffner Model of Reduction: A Bayesian Analysis.Marko Tešić - 2019 - Synthese 196 (3):1097-1129.
    In their 2010 paper, Dizadji-Bahmani, Frigg, and Hartmann argue that the generalized version of the Nagel–Schaffner model that they have developed is the right one for intertheoretic reduction, i.e. the kind of reduction that involves theories with largely overlapping domains of application. Drawing on the GNS, DFH presented a Bayesian analysis of the confirmatory relation between the reducing theory and the reduced theory and argued that, post-reduction, evidence confirming the reducing theory also confirms the reduced theory and evidence confirming the (...)
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  • PRM Inference Using Jaffray & Faÿ’s Local Conditioning.Christophe Gonzales & Pierre-Henri Wuillemin - 2011 - Theory and Decision 71 (1):33-62.
    Probabilistic Relational Models (PRMs) are a framework for compactly representing uncertainties (actually probabilities). They result from the combination of Bayesian Networks (BNs), Object-Oriented languages, and relational models. They are specifically designed for their efficient construction, maintenance and exploitation for very large scale problems, where BNs are known to perform poorly. Actually, in large-scale problems, it is often the case that BNs result from the combination of patterns (small BN fragments) repeated many times. PRMs exploit this feature by defining these patterns (...)
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  • Decision Support Systems for Police: Lessons From the Application of Data Mining Techniques to “Soft” Forensic Evidence. [REVIEW]Giles Oatley, Brian Ewart & John Zeleznikow - 2006 - Artificial Intelligence and Law 14 (1-2):35-100.
    The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” evidence such (...)
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  • Confirmation and Reduction: A Bayesian Account.Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2011 - Synthese 179 (2):321-338.
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We (...)
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  • Models in Systems Medicine.Jon Williamson - 2017 - Disputatio 9 (47):429-469.
    Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective (...)
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  • Intelligent Diagnosis Systems.K. Balakrishnan & V. Honavar - 1998 - Journal of Intelligent Systems 8 (3-4):239-290.
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  • A Uniformly Consistent Estimator of Causal Effects Under the K-Triangle-Faithfulness Assumption.Peter Spirtes & Jiji Zhang - unknown
    Spirtes, Glymour and Scheines [Causation, Prediction, and Search Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 491–515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. Subsequently, Kalisch and B¨uhlmann (...)
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  • The Bi-Directional Relationship Between Source Characteristics and Message Content.Peter J. Collins, Ulrike Hahn, Ylva von Gerber & Erik J. Olsson - 2015 - Frontiers in Psychology 9.
    Much of what we believe we know, we know through the testimony of others. While there has been long-standing evidence that people are sensitive to the characteristics of the sources of testimony, for example in the context of persuasion, researchers have only recently begun to explore the wider implications of source reliability considerations for the nature of our beliefs. Likewise, much remains to be established concerning what factors influence source reliability. In this paper, we examine, both theoretically and empirically, the (...)
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  • Bertrand’s Paradox and the Maximum Entropy Principle.Nicholas Shackel & Darrell P. Rowbottom - forthcoming - Philosophy and Phenomenological Research.
    An important suggestion of objective Bayesians is that the maximum entropy principle can replace a principle which is known to get into paradoxical difficulties: the principle of indifference. No one has previously determined whether the maximum entropy principle is better able to solve Bertrand’s chord paradox than the principle of indifference. In this paper I show that it is not. Additionally, the course of the analysis brings to light a new paradox, a revenge paradox of the chords, that is unique (...)
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  • Reversing 30 Years of Discussion: Why Causal Decision Theorists Should One-Box.Wolfgang Spohn - 2012 - Synthese 187 (1):95-122.
    The paper will show how one may rationalize one-boxing in Newcomb's problem and drinking the toxin in the Toxin puzzle within the confines of causal decision theory by ascending to so-called reflexive decision models which reflect how actions are caused by decision situations (beliefs, desires, and intentions) represented by ordinary unreflexive decision models.
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  • Common Bayesian Models for Common Cognitive Issues.Francis Colas, Julien Diard & Pierre Bessière - 2010 - Acta Biotheoretica 58 (2-3):191-216.
    How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common (...)
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  • Symmetries and Asymmetries in Evidential Support.Ellery Eells & Branden Fitelson - 2002 - Philosophical Studies 107 (2):129 - 142.
    Several forms of symmetry in degrees of evidential support areconsidered. Some of these symmetries are shown not to hold in general. This has implications for the adequacy of many measures of degree ofevidential support that have been proposed and defended in the philosophical literature.
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  • Causality, Propensity, and Bayesian Networks.Donald Gillies - 2002 - Synthese 132 (1-2):63 - 88.
    This paper investigates the relations between causality and propensity. Aparticular version of the propensity theory of probability is introduced, and it is argued that propensities in this sense are not causes. Some conclusions regarding propensities can, however, be inferred from causal statements, but these hold only under restrictive conditions which prevent cause being defined in terms of propensity. The notion of a Bayesian propensity network is introduced, and the relations between such networks and causal networks is investigated. It is argued (...)
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  • Quantum Physical Symbol Systems.Kathryn Blackmond Laskey - 2006 - Journal of Logic, Language and Information 15 (1-2):109-154.
    Because intelligent agents employ physically embodied cognitive systems to reason about the world, their cognitive abilities are constrained by the laws of physics. Scientists have used digital computers to develop and validate theories of physically embodied cognition. Computational theories of intelligence have advanced our understanding of the nature of intelligence and have yielded practically useful systems exhibiting some degree of intelligence. However, the view of cognition as algorithms running on digital computers rests on implicit assumptions about the physical world that (...)
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  • Sets of Probability Distributions, Independence, and Convexity.Fabio G. Cozman - 2012 - Synthese 186 (2):577-600.
    This paper analyzes concepts of independence and assumptions of convexity in the theory of sets of probability distributions. The starting point is Kyburg and Pittarelli’s discussion of “convex Bayesianism” (in particular their proposals concerning E-admissibility, independence, and convexity). The paper offers an organized review of the literature on independence for sets of probability distributions; new results on graphoid properties and on the justification of “strong independence” (using exchangeability) are presented. Finally, the connection between Kyburg and Pittarelli’s results and recent developments (...)
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  • Tractable Inference for Probabilistic Data Models.Lehel Csato, Manfred Opper & Ole Winther - 2003 - Complexity 8 (4):64-68.
  • Bayes' Theorem.James Joyce - 2008 - Stanford Encyclopedia of Philosophy.
    Bayes' Theorem is a simple mathematical formula used for calculating conditional probabilities. It figures prominently in subjectivist or Bayesian approaches to epistemology, statistics, and inductive logic. Subjectivists, who maintain that rational belief is governed by the laws of probability, lean heavily on conditional probabilities in their theories of evidence and their models of empirical learning. Bayes' Theorem is central to these enterprises both because it simplifies the calculation of conditional probabilities and because it clarifies significant features of subjectivist position. Indeed, (...)
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  • A Ranking‐Theoretic Approach to Conditionals.Wolfgang Spohn - 2013 - Cognitive Science 37 (6):1074-1106.
    Conditionals somehow express conditional beliefs. However, conditional belief is a bi-propositional attitude that is generally not truth-evaluable, in contrast to unconditional belief. Therefore, this article opts for an expressivistic semantics for conditionals, grounds this semantics in the arguably most adequate account of conditional belief, that is, ranking theory, and dismisses probability theory for that purpose, because probabilities cannot represent belief. Various expressive options are then explained in terms of ranking theory, with the intention to set out a general interpretive scheme (...)
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  • Cultural Blankets: Epistemological Pluralism in the Evolutionary Epistemology of Mechanisms.Pierre Poirier, Luc Faucher & Jean-Nicolas Bourdon - forthcoming - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie:1-16.
    In a recently published paper, we argued that theories of cultural evolution can gain explanatory power by being more pluralistic. In his reply to it, Dennett agreed that more pluralism is needed. Our paper’s main point was to urge cultural evolutionists to get their hands dirty by describing the fine details of cultural products and by striving to offer detailed and, when explanatory, varied algorithms or mechanisms to account for them. While Dennett’s latest work on cultural evolution does marvelously well (...)
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  • Causal Models and the Acquisition of Category Structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
  • Cognitive Metaphor Theory and the Metaphysics of Immediacy.Mathias W. Madsen - 2016 - Cognitive Science 40 (4):881-908.
    One of the core tenets of cognitive metaphor theory is the claim that metaphors ground abstract knowledge in concrete, first-hand experience. In this paper, I argue that this grounding hypothesis contains some problematic conceptual ambiguities and, under many reasonable interpretations, empirical difficulties. I present evidence that there are foundational obstacles to defining a coherent and cognitively valid concept of “metaphor” and “concrete meaning,” and some general problems with singling out certain domains of experience as more immediate than others. I conclude (...)
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  • How to Determine the Boundaries of the Mind: A Markov Blanket Proposal.Michael D. Kirchhoff & Julian Kiverstein - forthcoming - Synthese.
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  • Modelling Mechanisms with Causal Cycles.Brendan Clarke, Bert Leuridan & Jon Williamson - 2014 - Synthese 191 (8):1-31.
    Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical (...)
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  • Subjective Probability as Sampling Propensity.Thomas Icard - 2016 - Review of Philosophy and Psychology 7 (4):863-903.
    Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and (...)
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  • How to Knit Your Own Markov Blanket.Andy Clark - 2017 - Philosophy and Predictive Processing.
    Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of (...)
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  • How to Entrain Your Evil Demon.Jakob Hohwy - 2017 - Philosophy and Predictive Processing.
    The notion that the brain is a prediction error minimizer entails, via the notion of Markov blankets and self-evidencing, a form of global scepticism — an inability to rule out evil demon scenarios. This type of scepticism is viewed by some as a sign of a fatally flawed conception of mind and cognition. Here I discuss whether this scepticism is ameliorated by acknowledging the role of action in the most ambitious approach to prediction error minimization, namely under the free energy (...)
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  • Almost Ideal: Computational Epistemology and the Limits of Rationality for Finite Reasoners.Danilo Fraga Dantas - 2016 - Dissertation, University of California, Davis
    The notion of an ideal reasoner has several uses in epistemology. Often, ideal reasoners are used as a parameter of (maximum) rationality for finite reasoners (e.g. humans). However, the notion of an ideal reasoner is normally construed in such a high degree of idealization (e.g. infinite/unbounded memory) that this use is unadvised. In this dissertation, I investigate the conditions under which an ideal reasoner may be used as a parameter of rationality for finite reasoners. In addition, I present and justify (...)
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  • Preemption in Singular Causation Judgments: A Computational Model.Simon Stephan & Michael R. Waldmann - 2018 - Topics in Cognitive Science 10 (1):242-257.
    Causal queries about singular cases are ubiquitous, yet the question of how we assess whether a particular outcome was actually caused by a specific potential cause turns out to be difficult to answer. Relying on the causal power framework, Cheng and Novick () proposed a model of causal attribution intended to help answer this question. We challenge this model, both conceptually and empirically. We argue that the central problem of this model is that it treats causal powers that are probabilistically (...)
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  • Reasoning With Causal Cycles.Bob Rehder - 2017 - Cognitive Science 41 (S5):944-1002.
    This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links (...)
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  • A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.Hongjing Lu, Randall R. Rojas, Tom Beckers & Alan L. Yuille - 2016 - Cognitive Science 40 (2):404-439.
    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...)
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  • The Markov Blankets of Life: Autonomy, Active Inference and the Free Energy Principle.Michael David Kirchhoff - 2018 - Journal of the Royal Society Interface 15 (138).
    This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an (...)
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  • Causal Argument.Ulrike Hahn, Frank Zenker & Roland Bluhm - 2017 - In Michael R. Waldmann (ed.), The Oxford Handbook of Causal Reasoning. New York, NY: Oxford University Press. pp. 475-494.
    In this chapter, we outline the range of argument forms involving causation that can be found in everyday discourse. We also survey empirical work concerned with the generation and evaluation of such arguments. This survey makes clear that there is presently no unified body of research concerned with causal argument. We highlight the benefits of a unified treatment both for those interested in causal cognition and those interested in argumentation, and identify the key challenges that must be met for a (...)
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  • Representation Theorems and Realism About Degrees of Belief.Lyle Zynda - 2000 - Philosophy of Science 67 (1):45-69.
    The representation theorems of expected utility theory show that having certain types of preferences is both necessary and sufficient for being representable as having subjective probabilities. However, unless the expected utility framework is simply assumed, such preferences are also consistent with being representable as having degrees of belief that do not obey the laws of probability. This fact shows that being representable as having subjective probabilities is not necessarily the same as having subjective probabilities. Probabilism can be defended on the (...)
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  • Analytic Idealism: A Consciousness-Only Ontology.Bernardo Kastrup - 2019 - Dissertation, Radboud University Nijmegen
    This thesis articulates an analytic version of the ontology of idealism, according to which universal phenomenal consciousness is all there ultimately is, everything else in nature being reducible to patterns of excitation of this consciousness. The thesis’ key challenge is to explain how the seemingly distinct conscious inner lives of different subjects—such as you and me—can arise within this fundamentally unitary phenomenal field. Along the way, a variety of other challenges are addressed, such as: how we can reconcile idealism with (...)
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  • First Principles in the Life Sciences: The Free-Energy Principle, Organicism, and Mechanism.Matteo Colombo & Cory Wright - forthcoming - Synthese:1-26.
    The free-energy principle claims that biological systems behave adaptively maintaining their physical integrity only if they minimize the free energy of their sensory states. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, and function of the brain, and has been called a “postulate,” a “mandatory principle,” and an “imperative.” While it might afford a theoretical foundation for understanding the complex relationship between physical environment, life, and mind, its epistemic (...)
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  • The Limits of Piecemeal Causal Inference.Conor Mayo-Wilson - 2014 - British Journal for the Philosophy of Science 65 (2):213-249.
    In medicine and the social sciences, researchers must frequently integrate the findings of many observational studies, which measure overlapping collections of variables. For instance, learning how to prevent obesity requires combining studies that investigate obesity and diet with others that investigate obesity and exercise. Recently developed causal discovery algorithms provide techniques for integrating many studies, but little is known about what can be learned from such algorithms. This article argues that there are causal facts that one could learn by conducting (...)
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  • Probability Kinematics and Probability Dynamics.Lydia McGrew - 2010 - Journal of Philosophical Research 35:89-105.
    Richard Jeffrey developed the formula for probability kinematics with the intent that it would show that strong foundations are epistemologically unnecessary. But the reasons that support strong foundationalism are considerations of dynamics rather than kinematics. The strong foundationalist is concerned with the origin of epistemic force; showing how epistemic force is propagated therefore cannot undermine his position. The weakness of personalism is evident in the difficulty the personalist has in giving a principled answer to the question of when the conditions (...)
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  • Perspectival Plurality, Relativism, and Multiple Indexing.Dan Zeman - 2018 - In Rob Truswell, Chris Cummins, Caroline Heycock, Brian Rabern & Hannah Rohde (eds.), Proceedings of Sinn und Bedeutung 21, Vol. 2. Semantics Archives. pp. 1353-1370.
    In this paper I focus on a recently discussed phenomenon illustrated by sentences containing predicates of taste: the phenomenon of " perspectival plurality " , whereby sentences containing two or more predicates of taste have readings according to which each predicate pertains to a different perspective. This phenomenon has been shown to be problematic for (at least certain versions of) relativism. My main aim is to further the discussion by showing that the phenomenon extends to other perspectival expressions than predicates (...)
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  • Robot Location Estimation in the Situation Calculus.Vaishak Belle & Hector J. Levesque - 2015 - Journal of Applied Logic 13 (4):397-413.
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  • Bayesian Generic Priors for Causal Learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
  • A Rational Analysis of the Selection Task as Optimal Data Selection.Mike Oaksford & Nick Chater - 1994 - Psychological Review 101 (4):608-631.
  • Analogical and Category-Based Inference: A Theoretical Integration with Bayesian Causal Models.Keith J. Holyoak, Hee Seung Lee & Hongjing Lu - 2010 - Journal of Experimental Psychology: General 139 (4):702-727.
  • The Role of Causality in Judgment Under Uncertainty.Tevye R. Krynski & Joshua B. Tenenbaum - 2007 - Journal of Experimental Psychology: General 136 (3):430-450.
  • Predictive and Diagnostic Learning Within Causal Models: Asymmetries in Cue Competition.Michael R. Waldmann & Keith J. Holyoak - 1992 - Journal of Experimental Psychology: General 121 (2):222-236.
  • Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...)
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  • Novelty and Inductive Generalization in Human Reinforcement Learning.Samuel J. Gershman & Yael Niv - 2015 - Topics in Cognitive Science 7 (3):391-415.
    In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and (...)
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